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Influence, Spinfluence: Rank-and-file are far more likely to start a contagion

Influence, Spinfluence: The rank-and-file citizen is far more likely to start a contagion.

If you’re heavy into social media and digital marketing, you’re probably familiar, intrigued or even obsessed with Klout, a Silicon Valley startup attempting to become a standard measure of online influence.Â It’s a fascinating service, and I admire the team’s momentum in quickly amassing industry adopters.

According to Klout, itsÂ Klout Score measures influence based on your ability to drive action. Every time you create content or engage you influence others. The Klout Score uses data from social networks in order to measure:

True Reach: How many people you influence

Amplification: How much you influence them

Network Impact: The influence of your network

This certainly can be valuable information, butÂ the concept of a “standard measure of influence” runs into problems if you expect that such a yardstick should have the ability to predict who will be influential, or who will seed a trend.

Research Magazine reported on a recent presentation by Facebook’s head of measurement research, Sean Bruic, whoÂ argues thatÂ the popular Gladwellian model of powerful influencers doesnâ€™t work.

His own analysis of Facebook suggests that influence isnâ€™t top-down. Rather, it diffuses across networks via several thousand small platforms, not one or two big ones. Most important, thereâ€™s no consistent predictor of which content or â€œlikesâ€ will become viral.Â On the other hand, Bruich claimed that small-scale social proof â€“ seeing that your friends have seen ads â€“ increases recall and purchase intention.

Another case (via Noah Brier) involves social media optimization company SocialFlow, which analyzed 14.8 million Tweets sent in the hours after bin Ladenâ€™s death. They that foundÂ Keith Urbahn wasnâ€™t the first to speculate Bin Ladenâ€™s death, but he was the one who gained the most trust and traction from the network. According to SocialFlow:

Before May 1st, not even the smartest of machine learning algorithms could have predicted Keith Urbahnâ€™s online relevancy score, or his potential to spark an incredibly viral information flow. While politicos â€œin the knowâ€ certainly knew him or of him, his previous interactions and size and nature of his social graph did little to reflect his potential to generate thousands of peopleâ€™s willingness to trust within a matter of minutes.

While connections, authority, trust and persuasiveness play a key role in influencing others, they are only part of a complex set of dynamics that affect peopleâ€™s perception of a person, a piece of information or a product. Timing, initiating a network effect at the right time, and frankly, a dash of pure luck matter equally.

SocialFlow’s caution on influence scores:

As we build out digital social spaces, we must not get derailed by metrics of status affordances that have taken center stage. Just because we have easily accessible data at our fingertips doesnâ€™t mean that we have the capacity to model and place a value tag on human behavior. Followers, friends or likes represent an aspect of our digital status, but are only a partial representation of our general propensity to be influential.

Watts [built] aÂ Sims-like computer simulation, where he programmed a group of 10,000 people, all governed by a few simple interpersonal rules. Each was able to communicate with anyone nearby. With every contact, each had a small probability of “infecting” another. And each person also paid attention to what was happening around him: If lots of other people were adopting a trend, he would be more likely to join, and vice versa. The “people” in the virtual society had varying amounts of sociability–some were more connected than others. Watts designated the top 10% most-connected as Influentials; they could affect four times as many people as the average Joe. In essence, it was a virtual society run–in a very crude fashion–according to the rules laid out by thinkers like Gladwell…Watts set the test in motion by randomly picking one person as a trendsetter, then sat back to see if the trend would spread. He did so thousands of times in a row.

The results were deeply counterintuitive. The experiment did produce several hundred societywide infections. But in the large majority of cases, the cascade began with an average Joe (although in cases where an Influential touched off the trend, it spread much further). To stack the deck in favor of Influentials, Watts changed the simulation, making them 10 times more connected. Now they could infect 40 times more people than the average citizen (and again, when they kicked off a cascade, it was substantially larger). But the rank-and-file citizen was still far more likely to start a contagion.

So how do you harness influence? Fast Company describes Watts’s Big Seed idea:

Cascades require word-of-mouth effects, so you need to build a six-degrees effect into an ad campaign; but since you can never know which person is going to spark the fire, you should aim the ad at as broad a market as possible–and not waste money chasing “important” people. And it worked. The pass-around effect doubled the number of people who saw the [test] ad. They paid for 22,582 hits and received an additional 31,590 for free. Another campaign they ran for the Oxygen network quadrupled the audience size, adding 23,544 hits to the initial 7,064.

As for Klout, I strongly believe they are onto something, though I’m not sure the Klout Score is a precise standard of influence. It’s more reflective of someone’s history of content engagement, which is clearly different than one’s propensity to seed a trend.

The question is: How do you apply those metrics of content engagement to drive an advantageous marketing outcome?